1. Analysis and Workflows Lesson 12: Analysis and Workflows CC image by Marc_Smith on Flickr

2. Analysis and Workflows Review of typical data analyses Reproducibility & provenance Workflows in general Computer-based scientific workflows (SWF) Benefits of SWF Examples of SWF and associated tools

3. Analysis and Workflows After completing this lesson, the participant will be able to: o Understand a subset of typical analyses used o Define a workflow o Define a SWF o Discuss the benefits of workflows in general and SWF in particular o Locate resources for using SWF

4. Analysis and Workflows Plan Collect AssureDescribePreserveDiscoverIntegrateAnalyze

5. Analysis and Workflows Conducted via personal computer, grid, cloud computing Statistics, model runs, parameter estimations, graphs/plots etc.

6. Analysis and Workflows Processing: subsetting, merging, manipulating ◦ Reduction: important for high-resolution datasets ◦ Transformation: unit conversions, linear and nonlinear algorithms 0711070500276000 0711070600276000 0711070700277003 0711070800282017 0711070900285000 0711071000293000 0711071100301000 0711071200304000 Datetimeair tempprecip Cmm 11-Jul-075:0027.6000 11-Jul-076:0027.6000 11-Jul-077:0027.7003 11-Jul-078:0028.2017 11-Jul-079:0028.5000 11-Jul-0710:0029.3000 11-Jul-0711:0030.1000 11-Jul-0712:0030.4000 Recreated from Michener & Brunt (2000)

7. Analysis and Workflows Graphical analyses o Visual exploration of data: search for patterns o Quality assurance: outlier detection Box and whisker plot of temperature by month Scatter plot of August Temperatures Strasser, unpub. data

8. Analysis and Workflows Statistical analyses Conventional statistics o Experimental data o Examples: ANOVA, MANOVA, linear and nonlinear regression o Rely on assumptions: random sampling, random & normally distributed error, independent error terms, homogeneous variance Descriptive statistics o Observational or descriptive data o Examples: diversity indices, cluster analysis, quadrant variance, distance methods, principal component analysis, correspondence analysis From Oksanen (2011) Multivariate Analysis of Ecological Communities in R: vegan tutorial Example of Principle Component Analysis

9. Analysis and Workflows Statistical analyses (continued) o Temporal analyses: time series o Spatial analyses: for spatial autocorrelation o Nonparametric approaches useful when conventional assumptions violated or underlying distribution unknown o Other misc. analyses: risk assessment, generalized linear models, mixed models, etc. Analyses of very large datasets o Data mining & discovery o Online data processing

10. Analysis and Workflows Re-analysis of outputs Final visualizations: charts, graphs, simulations etc. Science is iterative: The process that results in the final product can be complex

11. Analysis and Workflows Reproducibility at core of scientific method Complex process = more difficult to reproduce Good documentation required for reproducibility o Metadata: data about data o Process metadata: data about process used to create, manipulate, and analyze data CC image by Felix63 on Flickr

12. Analysis and Workflows Information about process used to get to data outputs Related concept: data provenance o Origins of data o Good provenance = able to follow data throughout entire life cycle o Allows for Replication & reproducibility Analysis for potential defects, errors in logic, statistical errors Evaluation of hypotheses

13. Analysis and Workflows Formalization of process metadata Precise description of scientific procedure Conceptualized series of data ingestion, transformation, and analytical steps Three components o Inputs: information or material required o Outputs: information or material produced & potentially used as input in other steps o Transformation rules/algorithms (e.g. analyses)

14. Analysis and Workflows Simplest form of workflow: flow chart Data import into R Analysis: mean, SD Graph production Quality control & data cleaning

15. Analysis and Workflows Simplest form of workflow: flow chart Transformation Rules Data import into R Analysis: mean, SD Graph production Quality control & data cleaning

16. Analysis and Workflows Simplest form of workflow: flow chart Temperature data Salinity data Data import into R Analysis: mean, SD Quality control & data cleaning “Clean” T & S data Inputs & Outputs Summary statistics Data in R format

17. Analysis and Workflows Science is becoming more computationally intensive Sharing workflows benefits science o Scientific workflow systems make documenting workflows easier Simplest workflows: scripted languages

18. Analysis and Workflows Analytical pipeline Each step can be implemented in different software systems Each step & its parameters/requirements formally recorded Allows reuse of both individual steps and overall workflow

19. Analysis and Workflows Single access point for multiple analyses across software packages Keeps track of analysis and provenance: enables reproducibility o Each step & its parameters/requirements formally recorded Workflow can be stored Allows sharing and reuse of individual steps or overall workflow o Automate repetitive tasks o Use across different disciplines and groups o Can run analyses more quickly since not starting from scratch

20. Analysis and Workflows Open-source, free, cross-platform Drag-and-drop interface for workflow construction Steps (analyses, manipulations etc) in workflow represented by “actor” Actors connect from a workflow Possible applications o Theoretical models or observational analyses o Hierarchical modeling o Can have nested workflows o Can access data from web-based sources (e.g. databases) Downloads and more information at kepler-project.org

21. Analysis and Workflows Drag & drop components from this list Actors in workflow

22. Analysis and Workflows This model shows the solution to the classic Lotka- Volterra predator prey dynamics model. It uses the Continuous Time domain to solve two coupled differential equations, one that models the predator population and one that models the prey population. The results are plotted as they are calculated showing both population change and a phase diagram of the dynamics.

23. Analysis and Workflows Resulting output

24. Analysis and Workflows Open-source Workflow & provenance management support Geared toward exploratory computational tasks o Can manage evolving SWF o Maintains detailed history about steps & data www.vistrails.org Screenshot example

25. Analysis and Workflows Social networking site to support scientists that use SWF Allows searching for, sharing, reuse of SWF Can comment on and discuss contributed SWF Gateway to journals and data repositories www.myexperiment.org

26. Analysis and Workflows Scientists should document workflows used to create results o Data provenance o Analyses and parameters used o Connections between analyses via inputs and outputs Documentation can be informal (e.g. flowchart) or formal (e.g. Kepler)

27. Analysis and Workflows Modern science is computer-intensive o Heterogeneous data, analyses, software Reproducibility is important Workflows = process metadata o Necessary for reproducibility, repeatability, validation SFW offers formal systems for documenting process metadata o Storage, sharing, visualization, reuse

28. Analysis and Workflows Y. Gil, E. Deelman, M. Ellisman, T. Fahringer, G. Fox et al. Examining the Challenges of Scientific Workflows. Computer 40,24–32 (2007). K. Michener, J. Beach, M. Jones, B. Ludäscher, D. Pennington et al. A knowledge environment for the biodiversity and ecological sciences. J. Intel. Info. Sys. 29, 111–126 (2007). B. Ludäscher, I. Altintas, S. Bowers, J. Cummings, T. Critchlow et al. Scientific Process Automation and Workflow Management. Comp. Sci. Ser. Ch 13 (Chapman and Hall, Boca Raton, 2009). T. McPhillips, S. Bowers, D. Zinn, B. Ludäscher. Scientific workflow design for mere mortals. Fut. Gen. Comp. Sys. 25, 541-551 (2009). B. Ludäscher, I. Altintas, C. Berkley, D. Higgins, E. Jaeger-Frank et al. Scientific workflow management and the kepler system. Conc. Comp. Prac. Exper., 18 (2006). W. Michener and J. Brunt, Eds. Ecological Data: Design, Management and Processing. (Blackwell, New York, 2000).

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